OpenRaiser/Intern-Atlas huggingface dataset:
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Recent works have found evidence of gender bias in models of machine translation and coreference resolution using mostly synthetic diagnostic datasets. While these quantify bias in a controlled experiment, they often do so on a small scale and consist mostly of artificial, out-of-distribution sentences. In this work, we find grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments (e.g., female nurses versus male dancers) in corpora from three domains, resulting in a first large-scale gender bias dataset of 108K diverse real-world English sentences. We manually verify the quality of our corpus and use it to evaluate gender bias in various coreference resolution and machine translation models. We find that all tested models tend to over-rely on gender stereotypes when presented with natural inputs, which may be especially harmful when deployed in commercial systems. Finally, we show that our dataset lends itself to finetuning a coreference resolution model, finding it mitigates bias on a held out set. Our dataset and models are publicly available at www.github.com/SLAB-NLP/BUG. We hope they will spur future research into gender bias evaluation mitigation techniques in realistic settings.
Automated LoD-2 Model Reconstruction from Very-HighResolution Satellite-derived Digital Surface Model and Orthophoto
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a model-driven method that reconstructs LoD-2 building models following a "decomposition-optimization-fitting" paradigm. The proposed method starts building detection results through a deep learning-based detector and vectorizes individual segments into polygons using a "three-step" polygon extraction method, followed by a novel grid-based decomposition method that decomposes the complex and irregularly shaped building polygons to tightly combined elementary building rectangles ready to fit elementary building models. We have optionally introduced OpenStreetMap (OSM) and Graph-Cut (GC) labeling to further refine the orientation of 2D building rectangle. The 3D modeling step takes building-specific parameters such as hip lines, as well as non-rigid and regularized transformations to optimize the flexibility for using a minimal set of elementary models. Finally, roof type of building models s refined and adjacent building models in one building segment are merged into the complex polygonal model. Our proposed method has addressed a few technical caveats over existing methods, resulting in practically high-quality results, based on our evaluation and comparative study on a diverse set of experimental datasets of cities with different urban patterns.
ELIT: Emory Language and Information Toolkit
Venue: arXiv cs.CL 2021
Year: 2021

We introduce ELIT, the Emory Language and Information Toolkit, which is a comprehensive NLP framework providing transformer-based end-to-end models for core tasks with a special focus on memory efficiency while maintaining state-of-the-art accuracy and speed. Compared to existing toolkits, ELIT features an efficient Multi-Task Learning (MTL) model with many downstream tasks that include lemmatization, part-of-speech tagging, named entity recognition, dependency parsing, constituency parsing, semantic role labeling, and AMR parsing. The backbone of ELIT's MTL framework is a pre-trained transformer encoder that is shared across tasks to speed up their inference. ELIT provides pre-trained models developed on a remix of eight datasets. To scale up its service, ELIT also integrates a RESTful Client/Server combination. On the server side, ELIT extends its functionality to cover other tasks such as tokenization and coreference resolution, providing an end user with agile research experience. All resources including the source codes, documentation, and pre-trained models are publicly available at https://github.com/emorynlp/elit.
BMS: Secure Decentralized Reconfiguration for Blockchain and BFT Systems
Venue: arXiv cs.CR 2021
Year: 2021

Reconfiguration of long-lived blockchain and Byzantine fault-tolerant (BFT) systems poses fundamental security challenges. In case of state-of-the-art Proof-of-Stake (PoS) blockchains, stake reconfiguration enables so-called long-range attacks, which can lead to forks. Similarly, permissioned blockchain systems, typically based on BFT, reconfigure internally, which makes them susceptible to a similar "I still work here" attack. In this work, we propose BMS (Blockchain/BFT Membership Service) offering a secure and dynamic reconfiguration service for BFT and blockchain systems, preventing long-range and similar attacks. In particular: (1) we propose a root BMS for permissioned blockchains, implemented as an Ethereum smart contract and evaluate it reconfiguring the recently proposed Mir-BFT protocol, (2) we discuss how our BMS extends to PoS blockchains and how it can reduce PoS stake unbonding time from weeks/months to the order of minutes, and (3) we discuss possible extensions of BMS to hierarchical deployments as well as to multiple root BMSs.
A Formal Description of Sorani Kurdish Morphology
Venue: arXiv cs.CL 2021
Year: 2021

Sorani Kurdish, also known as Central Kurdish, has a complex morphology, particularly due to the patterns in which morphemes appear. Although several aspects of Kurdish morphology have been studied, such as pronominal endoclitics and Izafa constructions, Sorani Kurdish morphology has received trivial attention in computational linguistics. Moreover, some morphemes, such as the emphasis endoclitic =îş, and derivational morphemes have not been previously studied. To tackle the complex morphology of Sorani, we provide a thorough description of Sorani Kurdish morphological and morphophonological constructions in a formal way such that they can be used as finite-state transducers for morphological analysis and synthesis.
Any Regular Polyhedron Can Transform to Another by O(1) Refoldings
Venue: arXiv cs.CG 2021
Year: 2021

We show that several classes of polyhedra are joined by a sequence of O(1) refolding steps, where each refolding step unfolds the current polyhedron (allowing cuts anywhere on the surface and allowing overlap) and folds that unfolding into exactly the next polyhedron; in other words, a polyhedron is refoldable into another polyhedron if they share a common unfolding. Specifically, assuming equal surface area, we prove that (1) any two tetramonohedra are refoldable to each other, (2) any doubly covered triangle is refoldable to a tetramonohedron, (3) any (augmented) regular prismatoid and doubly covered regular polygon is refoldable to a tetramonohedron, (4) any tetrahedron has a 3-step refolding sequence to a tetramonohedron, and (5) the regular dodecahedron has a 4-step refolding sequence to a tetramonohedron. In particular, we obtain at most 6-step refolding sequence between any pair of Platonic solids, applying (5) for the dodecahedron and (1) and/or (2) for all other Platonic solids. As far as the authors know, this is the first result about common unfolding involving the regular dodecahedron.
Taxim: An Example-based Simulation Model for GelSight Tactile Sensors
Venue: arXiv cs.RO 2021
Year: 2021

Simulation is widely used in robotics for system verification and large-scale data collection. However, simulating sensors, including tactile sensors, has been a long-standing challenge. In this paper, we propose Taxim, a realistic and high-speed simulation model for a vision-based tactile sensor, GelSight. A GelSight sensor uses a piece of soft elastomer as the medium of contact and embeds optical structures to capture the deformation of the elastomer, which infers the geometry and forces applied at the contact surface. We propose an example-based method for simulating GelSight: we simulate the optical response to the deformation with a polynomial look-up table. This table maps the deformed geometries to pixel intensity sampled by the embedded camera. In order to simulate the surface markers' motion that is caused by the surface stretch of the elastomer, we apply the linear elastic deformation theory and the superposition principle. The simulation model is calibrated with less than 100 data points from a real sensor. The example-based approach enables the model to easily migrate to other GelSight sensors or its variations. To the best of our knowledge, our simulation framework is the first to incorporate marker motion field simulation that derives from elastomer deformation together with the optical simulation, creating a comprehensive and computationally efficient tactile simulation framework. Experiments reveal that our optical simulation has the lowest pixel-wise intensity errors compared to prior work and can run online with CPU computing. Our code and supplementary materials are open-sourced at https://github.com/CMURoboTouch/Taxim.
Bag of Tricks for Optimizing Transformer Efficiency
Venue: arXiv cs.LG 2021
Year: 2021

Improving Transformer efficiency has become increasingly attractive recently. A wide range of methods has been proposed, e.g., pruning, quantization, new architectures and etc. But these methods are either sophisticated in implementation or dependent on hardware. In this paper, we show that the efficiency of Transformer can be improved by combining some simple and hardware-agnostic methods, including tuning hyper-parameters, better design choices and training strategies. On the WMT news translation tasks, we improve the inference efficiency of a strong Transformer system by 3.80X on CPU and 2.52X on GPU. The code is publicly available at https://github.com/Lollipop321/mini-decoder-network.
Malware Sight-Seeing: Accelerating Reverse-Engineering via Point-of-Interest-Beacons
Venue: arXiv cs.CR 2021
Year: 2021

New types of malware are emerging at concerning rates. However, analyzing malware via reverse engineering is still a time-consuming and mostly manual task. For this reason, it is necessary to develop techniques that automate parts of the reverse engineering process and that can evade the built-in countermeasures of modern malware. The main contribution of this paper is a novel method to automatically find so-called Points-of-Interest (POIs) in executed programs. POIs are instructions that interact with data that is known to an analyst. They can be used as beacons in the analysis of malware and can help to guide the analyst to the interesting parts of the malware. Furthermore, we propose a metric for POIs , the so-called confidence score that estimates how exclusively a POI will process data relevant to the malware. With the goal of automatically extract peers in P2P botnet malware, we demonstrate and evaluate our approach by applying it on four botnets (ZeroAccess, Sality, Nugache, and Kelihos). We looked into the identified POIs for known IPs and ports and, by using this information, leverage it to successfully monitor the botnets. Furthermore, using our scoring system, we show that we can extract peers for each botnet with high accuracy.
Tiny CNN for feature point description for document analysis: approach and dataset
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that the specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset with a method of training patches retrieval. We prove the effectiveness of this data by training a lightweight neural network and show how it performs in both documents and general patches matching. The training was done on the provided dataset in comparison with HPatches training dataset and for the testing we use HPatches testing framework and two publicly available datasets with various documents pictured on complex backgrounds: MIDV-500 and MIDV-2019.
Automatic Timing-Coherent Transactor Generation for Mixed-level Simulations
Venue: arXiv cs.PF 2021
Year: 2021

In this paper we extend the concept of the traditional transactor, which focuses on correct content transfer, to a new timing-coherent transactor that also accurately aligns the timing of each transaction boundary so that designers can perform precise concurrent system behavior analysis in mixed-abstraction-level system simulations which are essential to increasingly complex system designs. To streamline the process, we also developed an automatic approach for timing-coherent transactor generation. Our approach is actually applied in mixed-level simulations and the results show that it achieves 100% timing accuracy while the conventional approach produces results of 25% to 44% error rate.
Safe, Deterministic Trajectory Planning for Unstructured and Partially Occluded Environments
Venue: arXiv cs.RO 2021
Year: 2021

Ensuring safe behavior for automated vehicles in unregulated traffic areas poses a complex challenge for the industry. It is an open problem to provide scalable and certifiable solutions to this challenge. We derive a trajectory planner based on model predictive control which interoperates with a monitoring system for pedestrian safety based on cellular automata. The combined planner-monitor system is demonstrated on the example of a narrow indoor parking environment. The system features deterministic behavior, mitigating the immanent risk of black boxes and offering full certifiability. By using fundamental and conservative prediction models of pedestrians the monitor is able to determine a safe drivable area in the partially occluded and unstructured parking environment. The information is fed to the trajectory planner which ensures the vehicle remains in the safe drivable area at any time through constrained optimization. We show how the approach enables solving plenty of situations in tight parking garage scenarios. Even though conservative prediction models are applied, evaluations indicate a performant system for the tested low-speed navigation.
Incentivizing an Unknown Crowd
Venue: arXiv cs.LG 2021
Year: 2021

Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a heterogeneous and unknown crowd. We propose a reinforcement learning-based approach that is effective against a wide range of settings including potential irrationality and collusion among workers. With the aid of a costly oracle and the inference method, our approach dynamically decides the oracle calls and gains robustness even under the presence of frequent collusion activities. Extensive experiments show the advantage of our approach. Our results also present the first comprehensive experiments of EIWV on large-scale real datasets and the first thorough study of the effects of environmental variables.
On Discrete Truthful Heterogeneous Two-Facility Location
Venue: arXiv cs.GT 2021
Year: 2021

We revisit the discrete heterogeneous two-facility location problem, in which there is a set of agents that occupy nodes of a line graph, and have private approval preferences over two facilities. When the facilities are located at some nodes of the line, each agent derives a cost that is equal to her total distance from the facilities she approves. The goal is to decide where to locate the two facilities, so as to (a) incentivize the agents to truthfully report their preferences, and (b) achieve a good approximation of the minimum total (social) cost or the maximum cost among all agents. For both objectives, we design deterministic strategyproof mechanisms with approximation ratios that significantly outperform the state-of-the-art, and complement these results with (almost) tight lower bounds.
GNisi: A graph network for reconstructing Ising models from multivariate binarized data
Venue: arXiv cs.LG 2021
Year: 2021

Ising models are a simple generative approach to describing interacting binary variables. They have proven useful in a number of biological settings because they enable one to represent observed many-body correlations as the separable consequence of many direct, pairwise statistical interactions. The inference of Ising models from data can be computationally very challenging and often one must be satisfied with numerical approximations or limited precision. In this paper we present a novel method for the determination of Ising parameters from data, called GNisi, which uses a Graph Neural network trained on known Ising models in order to construct the parameters for unseen data. We show that GNisi is more accurate than the existing state of the art software, and we illustrate our method by applying GNisi to gene expression data.
Estimation of Corporate Greenhouse Gas Emissions via Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals.
Tracking Turbulence Through Financial News During COVID-19
Venue: arXiv cs.CL 2021
Year: 2021

Grave human toll notwithstanding, the COVID-19 pandemic created uniquely unstable conditions in financial markets. In this work we uncover and discuss relationships involving sentiment in financial publications during the 2020 pandemic-motivated U.S. financial crash. First, we introduce a set of expert annotations of financial sentiment for articles from major American financial news publishers. After an exploratory data analysis, we then describe a CNN-based architecture to address the task of predicting financial sentiment in this anomalous, tumultuous setting. Our best performing model achieves a maximum weighted F1 score of 0.746, establishing a strong performance benchmark. Using predictions from our top performing model, we close by conducting a statistical correlation study with real stock market data, finding interesting and strong relationships between financial news and the S\&P 500 index, trading volume, market volatility, and different single-factor ETFs.
Degrees of randomized computability: decomposition into atoms
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we study structural properties of LV-degrees of the algebra of collections of sequences that are non-negligible in the sense that they can be computed by a probabilistic algorithm with positive probability. We construct atoms and infinitely divisible elements of this algebra generated by sequences, which cannot be Martin-Löf random and, moreover, these sequences cannot be Turing equivalent to random sequences. The constructions are based on the corresponding templates which can be used for defining the special LV-degrees. In particular, we present the template for defining atoms of the algebra of LV-degrees and obtain the decomposition of the maximal LV-degree into a countable sequence of atoms and their non-zero complement -- infinitely divisible LV-degree. We apply the templates to establish new facts about specific LV-degrees, such as the LV-degree of the collection of sequences of hyperimmune degree. We construct atoms defined by collections of hyperimmune sequences, moreover, a representation of LV-degree of the collection of all hyperimmune sequences will be obtained in the form of a union of an infinite sequence of atoms and an infinitely divisible element.
FGOT: Graph Distances based on Filters and Optimal Transport
Venue: arXiv cs.LG 2021
Year: 2021

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph distance. It is an optimal transport based distance which drives graph comparison through the probability distribution of filtered graph signals. This creates a highly flexible distance, capable of prioritising different spectral information in observed graphs, offering a wide range of choices for a comparison metric. We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem. We then propose a new approximate cost function that circumvents many computational difficulties inherent to graph comparison and permits the exploitation of fast algorithms such as mirror gradient descent, without grossly sacrificing the performance. We finally propose a novel algorithm derived from a stochastic version of mirror gradient descent, which accommodates the non-convexity of the alignment problem, offering a good trade-off between performance accuracy and speed. The experiments on graph alignment and classification show that the flexibility gained through filter graph distances can have a significant impact on performance, while the difference in speed offered by the approximation cost makes the framework applicable in practical settings.
Analysis of Language Change in Collaborative Instruction Following
Venue: arXiv cs.CL 2021
Year: 2021

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.
Object recognition for robotics from tactile time series data utilising different neural network architectures
Venue: arXiv cs.RO 2021
Year: 2021

Robots need to exploit high-quality information on grasped objects to interact with the physical environment. Haptic data can therefore be used for supplementing the visual modality. This paper investigates the use of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) neural network architectures for object classification on Spatio-temporal tactile grasping data. Furthermore, we compared these methods using data from two different fingertip sensors (namely the BioTac SP and WTS-FT) in the same physical setup, allowing for a realistic comparison across methods and sensors for the same tactile object classification dataset. Additionally, we propose a way to create more training examples from the recorded data. The results show that the proposed method improves the maximum accuracy from 82.4% (BioTac SP fingertips) and 90.7% (WTS-FT fingertips) with complete time-series data to about 94% for both sensor types.